The optimal design of a twenty-five bar space truss commonly involves multiple loading conditions acting on 4 node elements in the linear elastic model. In this paper, we describe the behavior of the truss system with our experimental loading conditions on five node elements subject to minimum displacement and stresses that are used to formulate the constrained nonlinear optimization problem. Numerical computations are developed with the objective of mass minimization and the best structural design is selected by applying the interior point method with the guidance of Matlab Optimization Toolbox. Our numerical results show the optimal values of cross-sectional areas, material densities, and internal forces which satisfy the minimum weight design. These results provide the appropriate mass to the experimental data and allow substantial changes in size, shape, and topology.

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In this article, we develop a model of forecasting credit and debit of pension funds of the NSIF in Cameroon. By using time series tools and relying on the ARMA model (Auto -Regressive Moving Average), we appropriately analyze and predict the main existing credit and debit of funds. The aim is to elaborate a model that is able to provide reliable information on credit and debit, mainly on the financial balance of the regime in order to guarantee and also ensure the management and financial planning of pension funds managed by the National Social Insurance Fund (NSIF) in Cameroon.

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The financial crisis that is currently shaking the world, particularly the successive failures of the major banks have brought the issue of banking risks, including credit risk, back to the forefront. This risk must now be managed by more sophisticated methods. In this paper we present two methods that allow us to establish two functions, namely Fisher discriminant analysis and logistic regression; these two functions allow us to evaluate the risk of non-repayment incurred by a bank in the light of our data. It emerges that Fisher discriminant analysis is more effective or efficient than logistic regression for the evaluation of the risk of non-repayment of credit. Discriminant analysis and logistic regression are two methods of credit risk management here the problem we are trying to solve is how to help banks choose the most efficient method between the latter two.